Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

/pdf/sequencing-and-scheduling-algorithms-and-complexity-lnutyrr3kz.pdf

Sequencing and scheduling: algorithms and complexity

2005년 대한 생화학·분자생물학회 하계학술대회를 다녀와서

https://ira.lib.polyu.edu.hk/bitstream/10397/1269/1/SetupSurvey-2006%28June-19%29.pdf

A survey of scheduling problems with setup times or costs

Complexity and Approximation

Unrelated parallel machine scheduling using local search

This paper considers the problem of scheduling jobs on unrelated parallel machines to minimize the makespan. Recovering Beam Search is a recently introduced method for obtaining approximate solutions to combinatorial optimization problems. A traditional Beam Search algorithm is a type of truncated branch and bound algorithm approach. However, Recovering Beam Search allows the possibility of correcting wrong decisions by replacing partial solutions with others. We develop a Recovering Beam Search algorithm for our unrelated parallel machine scheduling problem that requires polynomial time. Computational results show that it is able to generate approximate solutions for instances with large size (up to 1000 jobs) using a few minutes of computation time.

Makespan minimization for scheduling unrelated parallel machines: A recovering beam search approach

This paper considers several scheduling problems where deliveries are made in batches with each batch delivered to the customer in a single shipment. Various scheduling costs, which are based on the delivery times of the jobs, are considered. The objective is to minimize the scheduling cost plus the delivery cost, and both single and parallel machine environments are considered. For many combinations of these, we either provide efficient algorithms that minimize total cost or show that the problem is intractable. Our work has implications for the coordination of scheduling with batch delivery decisions to improve customer service.

The Coordination of Scheduling and Batch Deliveries

This paper considers the online scheduling of a single machine in which jobs arrive over time, and preemption is not allowed. The goal is to minimize the total weighted completion time. We show that a simple modification of the shortest weighted processing time rule has a competitive ratio of two. This result is established using a new proof technique that does not rely explicitly on a lower bound on the optimal objective function value. Because it is known that no online algorithm can have a competitive ratio of less than two, we have resolved the open issue of determining the minimum competitive ratio for this problem.

Online Scheduling of a Single Machine to Minimize Total Weighted Completion Time

In most classical scheduling models, it is assumed that a job is dispatched to a customer immediately after its processing completes. In many practical situations, however, a set of delivery dates may be fixed before any jobs are processed. This is particularly relevant where delivery is an expensive or complicated operation, for example, as with heavy machinery. A similar situation arises where customers find deliveries disruptive and thus require them to be made within a limited time interval that repeats periodically. A third possibility is that a periodic business function, for example, the supplier's billing cycle, effectively defines a delivery date, and includes all jobs that have been completed since the previous billing cycle. These situations are not adequately represented by classical scheduling models. We consider a variety of deterministic scheduling problems in which a job is dispatched to a customer at the earliest fixed delivery date that is no earlier than the completion time of its processing. Problems where the number of delivery dates is constant, and others where it is specified as part of data input, are studied. For almost all problems considered, we either provide an efficient algorithm or establish that such an algorithm is unlikely to exist. By doing so, we permit comparisons between the solvability of these fixed delivery date problems and of the corresponding classical scheduling problems.

Chris N. Potts

Papers

Unrelated parallel machine scheduling using local search

Makespan minimization for scheduling unrelated parallel machines: A recovering beam search approach

The Coordination of Scheduling and Batch Deliveries

Online Scheduling of a Single Machine to Minimize Total Weighted Completion Time

Scheduling with Fixed Delivery Dates